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Journal ArticleDOI

The analysis of unbalanced linear models with variance components

B. Engel
- 01 Dec 1990 - 
- Vol. 44, Iss: 4, pp 195-219
TLDR
In this paper, statistical inference for fixed effects, random effects, and components of variance in an unbalanced linear model with variance components was discussed, and Fisher scoring and the EM-algorithm were described.
Abstract
Statistical inference for fixed effects, random effects and components of variance in an unbalanced linear model with variance components will be discussed. Variance components will be estimated by Restricted Maximum Likelihood. Iterative procedures for computing the estimates, such as Fisher scoring and the EM-algorithm, are described.

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Citations
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Journal ArticleDOI

In vitro produced and cloned embryos: Effects on pregnancy, parturition and offspring

Th.A.M. Kruip, +1 more
- 01 Jan 1997 - 
TL;DR: In this paper, the authors present summary information from 30 data sets obtained worldwide (WW) on IVP and nuclear transfer in different cattle breeds, plus the preliminary results from a highly controlled field study (FS) undertaken by Holland Genetics (HG) on Holstein-Friesian IVP calves.
Journal ArticleDOI

The circadian rhythm of salivary cortisol in growing pigs : Effects of age, gender, and stress

TL;DR: The results of this study emphasize the importance of considering the circadian rhythmicity of cortisol, in relation to age, gender, and (time of) stressor application, when studying the cortisol response of animals to stressors.
Journal ArticleDOI

Efficient Analysis of Mixed Hierarchical and Cross-Classified Random Structures Using a Multilevel Model.

TL;DR: An efficient and straightforward procedure is described for specifying and estimating parameters of general mixed models which contain both hierarchical and crossed random factors.
Journal ArticleDOI

Personalities in female domesticated pigs: behavioural and physiological indications

TL;DR: In this article, a total of 128 gilts from 31 sows were systematically studied from birth to slaughter in two identical trials, and the inter-test consistencies were determined by relating the individual reactions of gilts to the backtest to various characteristics and responses to tests at a later age.
Journal ArticleDOI

A simple approach for the analysis of generalizea linear mixed models

B. Engel, +1 more
TL;DR: In this paper, a generalized linear mixed model (GLM) is introduced by incorporating additional random effects into the linear predictor of the generalized linear model structure, where parameters are estimated by a combination of quasi-likelihood and iterated MINQUE (minimum norm quadratic unbiased estimation).
References
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Journal ArticleDOI

Random-effects models for longitudinal data

Nan M. Laird, +1 more
- 01 Dec 1982 - 
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
Journal ArticleDOI

Recovery of inter-block information when block sizes are unequal

TL;DR: In this article, a modified maximum likelihood procedure is proposed for estimating intra-block and inter-block weights in the analysis of incomplete block designs with block sizes not necessarily equal, and the method consists of maximizing the likelihood, not of all the data, but of selected error contrasts.
Journal ArticleDOI

Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems

TL;DR: In this paper, the authors proposed a restricted maximum likelihood (reml) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects, and developed a satisfactory asymptotic theory for estimators of variance components.
Journal ArticleDOI

A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values

C.R. Henderson
- 01 Mar 1976 - 
TL;DR: In this article, the inverse of a numerator relationship matrix is needed for best linear unbiased prediction of breeding values, and a simple method for computing the elements of this inverse without computing the relationship matrix itself is presented.